Audit Data Analytics (ADA) with R

class: center, middle, inverse, title-slide

# Audit Data Analytics (ADA) with R
## Focus on Audit Effectiveness and Efficiency <img src="data:image/png;base64,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" width="10%">
### Stewart Li (MCom, Bsc, CA, ACCA) <a href="mailto:stewartlisg@gmail.com" class="email">stewartlisg@gmail.com</a>
### 2020-09-23

---

# Available tools <img src="../assets/logo_original.png" class="title-hex">

.pull-left[

**Tools needed to do our jobs**;

- Audit tools
- BI tools
- Data science tools

]

.pull-right[

**Challenging issues**;

- Complex formula
- Not flexible
- Meet all your needs

]

---

# Audit tools <img src="../assets/logo_original.png" class="title-hex">

.left-column[

[Source:] [2015 AuditNet Audit Data Analysis Software Survey](https://www.surveymonkey.com/results/SM-WYX6VMW2/)

]

.right-column[

]

---

# BI tools <img src="../assets/logo_original.png" class="title-hex">

.pull-left[

Integrate with R or Python;

- [Microsoft (PowerBI, Azure)](https://docs.microsoft.com/en-us/power-bi/desktop-r-visuals)
- [Tableau](https://www.tableau.com/learn/whitepapers/using-r-and-tableau)
- [Qlik](https://community.qlik.com/t5/Qlik-Server-Side-Extensions/Qliksense-and-R-integration/td-p/1444562)
- [Rapidminer](https://docs.rapidminer.com/latest/studio/operators/utility/scripting/execute_r.html)
- Many others

]

.pull-right[

![](../assets/bitools.png)

]

---

# Data science tools <img src="../assets/logo_original.png" class="title-hex">

[Source:] [Stats Package Wars](https://kieranhealy.org/blog/archives/2019/02/07/statswars/)

---

# Motivation <img src="../assets/logo_original.png" class="title-hex">

.pull-left[

* Data analytics for external auditors | [ ICAEW](https://www.icaew.com/international-accounting-and-auditing/international-standards-auditing/auditing-standards-and-guidance/international-auditing-perspectives/data-analytics-for-external-auditors)
* Basic audit data analytics with R | [CPA Canada](https://www.cpacanada.ca/en/members-area/profession-news/2018/july/adas-practical-learning)
* Rutgers Accounting Research Center and Continuous Auditing & Reporting Lab | [ Rutgers](http://www.business.rutgers.edu/faculty/miklos-vasarhelyi)
* DIGAUDIT | [NHH](https://www.nhh.no/en/research-centres/digaudit/about-digaudit/)
* KPMG Master of Accounting with Data and Analytics Program | [ KPMG](https://home.kpmg/us/en/home/insights/2018/02/kpmg-master-of-accounting-with-data-and-analytics-program.html)

]

.pull-right[

[Spreadsheet workflows in R](https://education.rstudio.com/blog/2020/08/spreadsheets-using-r/), a good example shows you why you should use programming tools to do the audit job.

]

---

# Training course <img src="../assets/logo_original.png" class="title-hex">

**Course structure**;

1. Introduction: installation setup.  
2. Basic programming: R (Python), Base R (Pandas), Rmarkdown (Jupyter).
3. Tidyverse: clean, manipulate, visualize, functions, modeling, report.

**Learning objectives**;

1. Understand basic programming.
2. Acquire intermediate data analytics skills in R.

**Course materials**;

```{r}
usethis::use_course("https://github.com/stewartli/ada_tutorial/archive/master.zip")
```

---

# Setup <img src="../assets/logo_original.png" class="title-hex">

[Download R](https://www.r-project.org/)     
[Download RStudio](https://rstudio.com/products/rstudio/download/)     
Download packages

```{r}
pkgs <- c('tidyverse', 'data.table', 'kableExtra')
lapply(pkgs, install.packages)
pkgs %in% installed.packages()
```

---

# Demo <img src="../assets/logo_original.png" class="title-hex">

```{r}
rmarkdown::run("./tutorials/8_demo.Rmd")
```

---

# Contact us <img src="../assets/logo_original.png" class="title-hex">

.font150.center[&#x1F914; &#x1F4A1; &#x1F913;]

.center[Stewart Li | stewartlisg@gmail.com]

.center[China | Singapore | New Zealand]